There are several future tasks are identified through the dissertation.
1. The method to evaluate the energy consumption of wireless sensor node in this work is at a higher level, which provides the number of days the sensor node can work until it turns off. An alternative approach would be using high accuracy bench digital meters, to provide real-time power consumption of the node, which can further strength our assumptions that RF wake-up mechanism is at advantage of other methods.
2. The experimental in Chapter 3 is based on the commercially available DJI drone and hence is not a platform suitable for developments. The Wi-Fi parameter on that platform is not in our control, thus we did not conduct experiments on how the Wi-Fi video feed quality and the power level for both Wi-Wi-Fi and Zigbee radio will affect the interference level. Further work can be done using a customizable UAV platform with different Wi-Fi modules.
3. It is noted that Chapter 3 focuses on defining the concept of the aerial-ground wireless sensing and investigating the network interference within such a novel network, realistic structural response and environmental data are not analyzed. The future efforts based on finding in this include the development of heterogeneous
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sensing including low-speed environmental sensing (e.g. temperature and moisture data at a rate of one data point per minute) and fast structural sensing (e.g.
acceleration data at a rate of 200 points per second), and the aerial real-time data acquisition and computing. The subsequent investigation will include the implementation of a decentralized sensing solution that is optimized for either a single large-scale structure or a geospatially-large structure clusters in an urban area. The implementation task further includes the edge-computing based data processing and system identification towards fully real-time flying, sensing, and delivering of structural health and condition assessment analytics.
4. Simulation in Chapter 5 is using Julia programming language, the code is not optimized for parallel computing, which results running on a core multi-threads CPU cannot improve the computation time. One possible improvement would be optimizing the coding for parallel computing.
5. The result on energy consumption indicate that across the different approach, the total energy difference is not huge compare to their total value. This may lead to a debate that whether it is worth of trying to compute for the optimization path since it may cost more energy for compute than it saves. We believe this can be further investigated by take the computing cost into consider and compare the energy cost to the UAV flying energy.
6. Some parameter provides to run the simulation in Chapter 5 are based on the experiments done in Chapter 4. Thus, it only provides one possible wireless packet loss model. We suggest other researches could conduct more experiments with
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different hardware setup to get different model of wireless packet loss relative to distance. Then apply our dynamic approach to evaluate the energy consumption results with our setup.
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